Scavenger hunt facilitation

ABSTRACT

Methods and systems for facilitating a scavenger hunt. The systems and methods described herein involve receiving at an interface a list of a plurality of attractions, and communicating the list of the plurality of attractions to at least one device associated with a participant over a network. Scavenger hunt participants may then gather imagery of the required attractions. The systems and methods described herein then involve receiving imagery from the at least one participant and executing at least one computer vision procedure to determine whether the received imagery includes at least one of the plurality of attractions.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of co-pending U.S.provisional application No. 62/771,542, filed on Nov. 26, 2018, theentire disclosure of which is incorporated by reference as if set forthin its entirety herein.

TECHNICAL FIELD

The present application generally relates to systems and methods forfacilitating a scavenger hunt and, more particularly but notexclusively, to systems and methods for facilitating a scavenger huntinvolving imagery gathered by one or more scavenger hunt participants.

BACKGROUND

People are always looking for fun and exciting ways to visit orotherwise experience locations, objects, places, items of interest, orother types of attractions. Often times, locations such as cities withmultiple tourist attractions offer maps to tourists that highlightcertain locations the tourists may be interested in visiting. Similarly,museums may issue brochures or maps highlighting exhibits for visitorsto see. However, these regions or locations of interest generally do notprovide exciting ways for people to visit or otherwise experience theseitems, objects, or locations of interest.

A need exists, therefore, for systems and methods that offer moreexciting ways to experience locations, objects, places, or items ofinterest.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription section. This summary is not intended to identify or excludekey features or essential features of the claimed subject matter, nor isit intended to be used as an aid in determining the scope of the claimedsubject matter.

In one aspect, embodiments relate to a method for facilitating ascavenger hunt. The method includes receiving at an interface a list ofa plurality of attractions, communicating the list of the plurality ofattractions to at least one device associated with a participant over anetwork, receiving imagery from the at least one participant over thenetwork, and executing, using a processor executing instructions storedon memory to determine whether the received imagery includes at leastone of the plurality of attractions, at least one of a computer visionprocedure to analyze content of the received imagery and a locationprocedure to detect where the imagery was gathered.

In some embodiments, the method further includes receiving location dataregarding the imagery for analysis by the location procedure to at leastassist in determining whether the received imagery includes at least oneof the plurality of attractions.

In some embodiments, executing the at least one computer visionprocedure further includes executing a neural network to determinecontent of the received imagery.

In some embodiments, the plurality of attractions include at least oneof a point of interest, a person, a monument, a landmark, a location,and a building.

In some embodiments, the method further includes receiving a requiredtime period, and determining whether the imagery was gathered during therequired time period.

In some embodiments, the received imagery includes at least one of aphotograph file, a live photograph file, and a video file.

In some embodiments, the method further includes executing an opticalcharacter recognition tool to identify text within the imagery andrecognize meaning of the identified text.

In some embodiments, the method further includes issuing a credit to theat least one participant upon determining the received imagery includesthe plurality of attractions.

In some embodiments the method further includes providing feedback to atleast one participant regarding whether the received imagery includes atleast one of the plurality of attractions.

According to another aspect, embodiments relate to a system forfacilitating a scavenger hunt. The system includes an interface forreceiving a list of a plurality of attractions; and a processorexecuting instructions stored on memory and configured to: communicatethe list of the plurality of attractions to at least one deviceassociated with a participant over a network, receive imagery from theat least one participant over the network, and execute, to determinewhether the received imagery includes at least one of the plurality ofattractions, a computer vision procedure to analyze content of thereceived imagery, and a location procedure to detect where the imagerywas gathered.

In some embodiments, the processor is further configured to receivelocation data regarding the imagery for analysis by the locationprocedure to at least assist in determining whether the received imageryincludes at least one of the plurality of attractions.

In some embodiments, the processor is further configured to execute aneural network to determine content of the received imagery.

In some embodiments, the plurality of attractions include at least oneof a point of interest, a person, a monument, a landmark, a location,and a building.

In some embodiments, the interface is further configured to receive arequired time period, and the processor is further configured todetermine whether the imagery was gathered during the required timeperiod.

In some embodiments, the received imagery includes at least one of aphotograph file, a live photograph file, and a video file.

In some embodiments, the processor is further configured to execute anoptical character recognition tool to identify text within the imageryand recognize meaning of the identified text.

In some embodiments, the system is further configured to issue a creditto the at least one participant upon determining the received imageryincludes the plurality of attractions.

In some embodiments, the processor is further configured to providefeedback to at least one participant regarding whether the receivedimagery includes at least one of the plurality of attractions.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting and non-exhaustive embodiments of this disclosure aredescribed with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates a system for facilitating a scavenger hunt inaccordance with one embodiment;

FIG. 2 illustrates the imagery analysis module of FIG. 1 in accordancewith one embodiment; and

FIG. 3 depicts a flowchart of a method for facilitating a scavenger huntin accordance with one embodiment.

DETAILED DESCRIPTION

Various embodiments are described more fully below with reference to theaccompanying drawings, which form a part hereof, and which show specificexemplary embodiments. However, the concepts of the present disclosuremay be implemented in many different forms and should not be construedas limited to the embodiments set forth herein; rather, theseembodiments are provided as part of a thorough and complete disclosure,to fully convey the scope of the concepts, techniques andimplementations of the present disclosure to those skilled in the art.Embodiments may be practiced as methods, systems or devices.Accordingly, embodiments may take the form of a hardware implementation,an entirely software implementation or an implementation combiningsoftware and hardware aspects. The following detailed description is,therefore, not to be taken in a limiting sense.

Reference in the specification to “one embodiment” or to “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiments is included in at least one exampleimplementation or technique in accordance with the present disclosure.The appearances of the phrase “in one embodiment” in various places inthe specification are not necessarily all referring to the sameembodiment. The appearances of the phrase “in some embodiments” invarious places in the specification are not necessarily all referring tothe same embodiments.

Some portions of the description that follow are presented in terms ofsymbolic representations of operations on non-transient signals storedwithin a computer memory. These descriptions and representations areused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. Suchoperations typically require physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical, magnetic or optical signals capable of being stored,transferred, combined, compared and otherwise manipulated. It isconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like. Furthermore, it is also convenient at times, torefer to certain arrangements of steps requiring physical manipulationsof physical quantities as modules or code devices, without loss ofgenerality.

However, all of these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise as apparentfrom the following discussion, it is appreciated that throughout thedescription, discussions utilizing terms such as “processing” or“computing” or “calculating” or “determining” or “displaying” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem memories or registers or other such information storage,transmission or display devices. Portions of the present disclosureinclude processes and instructions that may be embodied in software,firmware or hardware, and when embodied in software, may be downloadedto reside on and be operated from different platforms used by a varietyof operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each may be coupled to a computer system bus.Furthermore, the computers referred to in the specification may includea single processor or may be architectures employing multiple processordesigns for increased computing capability.

The processes and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may also be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform one or more method steps. The structure for avariety of these systems is discussed in the description below. Inaddition, any particular programming language that is sufficient forachieving the techniques and implementations of the present disclosuremay be used. A variety of programming languages may be used to implementthe present disclosure as discussed herein.

In addition, the language used in the specification has been principallyselected for readability and instructional purposes and may not havebeen selected to delineate or circumscribe the disclosed subject matter.Accordingly, the present disclosure is intended to be illustrative, andnot limiting, of the scope of the concepts discussed herein.

The embodiments described herein provide novel ways to create andfacilitate a scavenger hunt for one or more participants. A user mayfirst define parameters or requirements of a scavenger hunt, such as byspecifying certain objects, items, people, monuments, locations,landmarks, or the like (for simplicity, “attractions”). The list ofattractions may then be communicated to or otherwise viewed by one ormore scavenger hunt participants, who will then attempt to gatherimagery of the listed attractions. While this list may be communicatedto one or more participants over one or more networks, attraction listsof a scavenger hunt may be previously-stored or otherwise accessible toparticipants through an application. Accordingly, in the context of thepresent application, the attraction list may be communicated to theparticipants in a variety of ways.

Imagery gathered by the participant(s) may then be communicated to oneor more processors for analysis. The systems and methods describedherein may rely on any one or more of computer vision procedures,machine learning procedures, optical character recognition (OCR)procedures, landmark or object data, time data, location data, or thelike to analyze received imagery to determine whether the receivedimagery satisfies the scavenger hunt requirements.

Upon determining that one or more participants have gathered therequired imagery, the systems and methods described herein may issuesome type of reward to the successful participant(s). For example, thesystems and methods described herein may issue a monetary reward,credits, gift cards, cryptocurrency, or the like, to a scavenger huntparticipant that gathered the required imagery.

FIG. 1 illustrates a system 100 for facilitating a scavenger hunt inaccordance with one embodiment. The system 100 may include a user device102 executing a user interface 104 for presentation to a user 106. Theuser 106 may be a person interested in providing parameters for ascavenger hunt for one or more participants. For example, the user 106may specify requirements regarding imagery to be captured in connectionwith the scavenger hunt.

The scavenger hunt requirements may vary and may depend on a number offactors. One factor may be the general location at which a scavengerhunt is to occur. For example, if a scavenger hunt were to take place inWashington D.C., required imagery may include imagery of the WhiteHouse, the United States Capitol, the Washington Monument, and theJefferson Memorial.

As another example, a scavenger hunt may generally be concentrated in amuseum. In this case, the user 106 may specify certain exhibits at themuseum of which participants must gather imagery (assumingimagery-gathering is permitted at the museum).

The scavenger hunts in accordance with the embodiments described hereinare not limited to only capturing imagery of objects or items. Rather,scavenger hunts may require participants to gather imagery of people aswell. For example, a scavenger hunt concentrated at a sporting event mayrequire participants to gather imagery of a team's mascot.

A user 106 may define the scavenger hunt requirements in a variety ofways. In some embodiments, the user 106 may provide or otherwise selectpreviously-obtained imagery of the target attractions. For example, torequire imagery of a team's mascot as part of a scavenger hunt, the user106 may provide or otherwise select previously-gathered imagery of themascot. Similarly, to require imagery of the White House as part of ascavenger hunt, the user 106 may provide or otherwise select imagery ofthe White House.

In the context of the present application, the term “imagery” may referto photographs, videos, mini clips, animated photographs, motion photos,or the like. In the context of the present application, the term“imagery portion” or the like may refer to an individual imagery file,such as a single photograph or video. Accordingly, a scavenger hunt mayrequire several imagery portions, (e.g., one imagery portion of eachattraction). In some scavenger hunts, the user 106 may require that eachimagery portion includes the participant at the specified attraction.For example, a participant may be required to have their picture takenin front of an attraction as a “selfie” or by a fixed orphotographer-manned camera.

The user 106 may also define different branches or other sets ofrequirements for the scavenger hunt. That is, participants may haveoptions regarding which requirements they satisfy. For example, ascavenger hunt may require that participants either gather imagery ofattraction A, or require the participants gather imagery of attractionsB and C. Similarly, a requirement may be that a participant gatherimagery of attractions in a certain order, or require that participantsgather imagery of a certain attraction multiple times.

The user device 102 may be any hardware device capable of executing theuser interface 104. The user device 102 may be configured as a laptop,PC, tablet, mobile device, or the like. The exact configuration of theuser device 102 may vary as long as it can execute and present the userinterface 104 to the user 106.

The user device 102 may be in operable communication with one or moreprocessors 108. The processor(s) 108 may be any hardware device capableof executing instructions stored on memory 110 to accomplish theobjectives of the various embodiments described herein. The processor(s)108 may be implemented as software executing on a microprocessor, afield programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another similar device whether available now orinvented hereafter.

In some embodiments, such as those relying on one or more ASICs, thefunctionality described as being provided in part via software mayinstead be configured into the design of the ASICs and, as such, theassociated software may be omitted. The processor(s) 108 may beconfigured as part of the user device 102 on which the user interface104 executes, such as a laptop, or may be located on a differentcomputing device, perhaps at some remote location.

The processor 108 may execute instructions stored on memory 110 toprovide various modules to accomplish the objectives of the variousembodiments described herein. Specifically, the processor 108 mayexecute or otherwise include an interface 112, an attraction engine 114,an imagery analysis module 116, and a credit issuance module 118.

The memory 110 may be L1, L2, or L3 cache or RAM memory configurations.The memory 110 may include non-volatile memory such as flash memory,EPROM, EEPROM, ROM, and PROM, or volatile memory such as static ordynamic RAM, as discussed above. The exact configuration/type of memory110 may of course vary as long as instructions for facilitating ascavenger hunt can be executed by the processor 108 to accomplish thefeatures of various embodiments described herein.

The processor 108 may receive imagery from the user 106 as well as oneor more participants 120, 122, 124, and 126 over one or more networks128. The participants 120, 122, 124, and 126 are illustrated as devicessuch as laptops, smartphones, smartwatches and PCs, or any other type ofdevice accessible by a participant and configured with an imagerygathering device (e.g., a camera) to gather imagery.

The systems and methods may analyze the imagery received from the user106, participants 120, 122, 124, and 126, one or more databases 130, orsome combination thereof in conjunction with the scavenger hunt. Whenthe user 106 creates a scavenger hunt, they may specify who should beparticipants. For example, the user 106 may enter user names orcredentials of people to participate in the scavenger hunt. Aninvitation may then be sent to the participants, along with thespecified scavenger hunt requirements. The invitation may be sent, forexample, over email and include a link inviting the participant to jointhe scavenger hunt.

The present application largely describes embodiments in which the user106 of user device 102 defines parameters of a scavenger hunt, and thenparticipants 120-26 gather and submit the required imagery. However, insome embodiments, the user 106 may also participate in the scavengerhunt along with the other participants 120-26.

The network(s) 128 may link the various assets and components withvarious types of network connections. The network(s) 128 may becomprised of, or may interface to, any one or more of the Internet, anintranet, a Personal Area Network (PAN), a Local Area Network (LAN), aWide Area Network (WAN), a Metropolitan Area Network (MAN), a storagearea network (SAN), a frame relay connection, an Advanced IntelligentNetwork (AIN) connection, a synchronous optical network (SONET)connection, a digital T1, T3, E1, or E3 line, a Digital Data Service(DDS) connection, a Digital Subscriber Line (DSL) connection, anEthernet connection, an Integrated Services Digital Network (ISDN) line,a dial-up port such as a V.90, a V.34, or a V.34bis analog modemconnection, a cable modem, an Asynchronous Transfer Mode (ATM)connection, a Fiber Distributed Data Interface (FDDI) connection, aCopper Distributed Data Interface (CDDI) connection, or an optical/DWDMnetwork.

The network(s) 128 may also comprise, include, or interface to any oneor more of a Wireless Application Protocol (WAP) link, a Wi-Fi link, amicrowave link, a General Packet Radio Service (GPRS) link, a GlobalSystem for Mobile Communication G(SM) link, a Code Division MultipleAccess (CDMA) link, or a Time Division Multiple access (TDMA) link suchas a cellular phone channel, a Global Positioning System (GPS) link, acellular digital packet data (CDPD) link, a Research in Motion, Limited(RIM) duplex paging type device, a Bluetooth radio link, or an IEEE802.11-based link.

The database(s) 130 may store imagery and other data related to, forexample, certain people (e.g., their facial features), places, objectsof interest, items, or the like. In other words, the database(s) 130 maystore data regarding attractions so that the imagery analysis module 116(discussed below) can recognize these attractions in received imagery.The exact type of data stored in the database(s) 130 may vary as long asthe features of various embodiments described herein may beaccomplished.

In operation, a user 106 may specify one or more required attractionsvia the attraction engine 114. Specifically, the provided targets mayspecify the content required as part of a scavenger hunt. The attractionengine 114 may execute various sub-modules to define requirements of ascavenger hunt. These may include a person sub-module 130 to specify oneor more people to be in the imagery, a location sub-module 132 tospecify a location associated with the imagery, and an item sub-module134 to specify an item required in imagery. There may be overlap as towhether a certain item of interest qualifies as an item, object, orlocation. For example, the White House may be classified as an object aswell as a location.

The attraction engine 114 may also include a time sub-module 136 tospecify a time period during which imagery must be gathered. Often timesscavenger hunts may be required to be completed in a certain time ortime window. Accordingly, the time sub-module 136 may enable the user106 to specify time constraints for a scavenger hunt. For example, theuser 106 may require that a scavenger hunt be open for one hour. Thatis, once a scavenger hunt starts, the participants 120-26 have one hourto gather the required imagery. As another example, a user 106 maycreate a scavenger hunt and specify that it must be completed from 1:00PM-4:00 PM on a certain date.

The scavenger hunt requirements may be sent to the participants 120-26over the network(s) 128. The participants 120-26 may be informed of thescavenger hunt by any suitable communication means, such as through textmessage, email, SMS, or some other type of alert. The incoming messagemay inform the participant(s) they have been invited to participate inthe scavenger hunt, the timing of the scavenger hunt, attractions to becaptured as part of the scavenger hunt, as well as any otherrequirements or parameters of the scavenger hunt.

The participant(s) 120-26 may then gather imagery of the requiredattractions. The user participant(s) 120-26 may use any suitable imagerygathering device such as a mobile device to gather the required imagery.The participants 120-26 may activate a link associated with thescavenger hunt such that all gathered imagery is automatically sent tothe imagery analysis module 116 for analysis. Or, the gathered imagerymay be sent to the imagery analysis module 116 at the conclusion of thescavenger hunt (e.g., at the expiration of a defined time range).

The processor interface 112 may receive imagery from the participantdevices 120-26 and the user 106 (e.g., if the user 106 is participatingin the scavenger hunt) in a variety of formats. The imagery may be sentvia any suitable protocol or application such as, but not limited to,email, SMS text message, iMessage, Whatsapp, Facebook, Instragram,Snapchat, etc. The interface 112 may then communicate the imagery to theimagery analysis module 116. The imagery analysis module 116 may executeone or more various sub-modules to analyze the imagery received fromeach of the participants 120-26. FIG. 2 illustrates the imagery analysismodule 116 of FIG. 1 in more detail in accordance with one embodiment.The imagery analysis module 116 may include components that include, butare not limited to, occasions algorithms 202, a machine learning module204, a computer vision module 206, a metadata deserializer 208, a facedetection module 210, a facial recognition module 212, a face clusteringmodule 214, an object detection module 216, an object identificationmodule 218, a scene detection module 220, a scene identification module222, a location module 224, a scannable indicia module 226, a scoringmodule 228, and a feedback module 230. Any of these components of theimagery analysis module 116 may, alone or in some combination, analyzereceived imagery 232 to determine if the imagery 232 includes the listedattractions 234.

The occasions algorithms 202 may include algorithms that recognizecertain dates, calendar events, or other types of occasions such asthose defined by the previously-discussed templates. These mayrecognize, for example, certain calendar dates that correspond toholidays.

The machine learning module 204 may implement a variety of machinelearning procedures to identify the contents of received imagery 232.The machine learning module 204 may implement supervised machinelearning techniques as well as unsupervised machine learning techniques.

The computer vision module 206 may implement a variety of visiontechniques to analyze the content of the received imagery 232. Thesetechniques may include, but are not limited to, scale-invariant featuretransform (SIFT), speeded up robust feature (SURF) techniques, or thelike. The exact techniques used may vary as long as they can analyze thecontent of the received imagery 232 to accomplish the features ofvarious embodiments described herein.

The metadata deserializer 208 may receive a variety types of metadata(e.g., in a serialized form). This data may include, but is not limitedto, EXIF data that specifies the formats for the received imagery 232.The deserializer 208 may then deserialize the received metadata into itsdeserialized form.

The face detection module 210 may execute a variety of facial detectionprograms to detect the presence of faces (and therefore people) invarious imagery portions. The programs may include or be reliant onOPENCV and neural networks, for example. Again, these programs mayexecute on the user device 102 and/or on a server at a remote location.The exact techniques or programs may vary as long as they can detectfacial features in imagery to accomplish the features of variousembodiments described herein.

The facial recognition module 212 may execute a variety of facialrecognition programs to identify certain people in various imageryportions. The facial recognition module 212 may be in communication withone or more databases 130 that store data regarding people and theirfacial characteristics. The facial recognition module 212 may usegeometric-based approaches and/or photometric-based approaches, and mayuse techniques based on principal component analysis, lineardiscriminant analysis, elastic bunch graph matching, HMM, multilinearsubspace learning, or the like.

Face attributes detected by either the face detection module 210 or thefacial recognition module 212 may be Neural Network-generated facialembeddings and include, but are not limited to, Hasglasses, Hassmile,age, gender, and face coordinates for: pupilLeft, pupilRight, noseTip,mouthLeft, mouthRight, eyebrowLeftOuter, eyebrowLeftInner, eyeLeftOuter,eyeLeftTop, eyeLeftBottom, eyeLeftInner, eyebrowRightInner,eyebrowRightOuter, EyeRightInner, eyeRightTop eyeRightBottom,eyeRightOuter, noseRootLeft, noseRootRight, noseLeftAlarTop,noseRightAlarTop, noseLeftAlarOutTip, noseRightAlarOutTip, upperLipTop,upperLipBottom, underLipTop, underLipBottom.

The face clustering module 214 may, once the facial recognition module212 identifies a certain person or a group of people in an imageryportion, group the imagery portion as being part of imagery associatedwith the certain person or the certain group of people. That is, animagery portion may be one of many identified as including a certainperson or a certain group of people.

The object detection module 216 may detect various objects present in animagery portion. For example, the object detection module 216 mayexecute one or more of various techniques (e.g., using the computervision module 206) to distinguish between an object in an imageryportion and the background of an imagery portion.

The object identification module 218 may then classify or otherwiserecognize the object as a certain item. For example, the objectidentification module 218 may analyze objects (e.g., by their shape,size, color, etc.) to determine if they constitute a requiredattraction. The object identification module 218 may also compare dataregarding the detected objects (e.g., their shape and size) to data inthe database 130 to determine if the detected object matches an objectstored in the database 130 and therefore constitutes a requiredattraction.

The scene detection module 220 may gather data that corresponds to thescene of an imagery portion. This may include data that indicates thecontext of an imagery portion such as whether the imagery portionincludes people, was taken indoors, outdoors, during the day, during thenight, etc. This data may be useful in determining whether an imageryportion satisfies a scavenger hunt requirement.

The scene identification module 222 may be in communication with thescene detection module 220 and receive data regarding the scene of animagery portion. The scene identification module 222 may compare thereceived data to data in the database 130 to determine whether it isindicative of a certain context, which may be useful in determiningwhether an imagery portion satisfies a scavenger hunt requirement.

The location module 224 may receive location data related to thegathered imagery. For example, an imagery portion may be tagged withlocation data such as GPS data that relates to where the imagery portionwas taken. This location data may be gathered through any appropriateGPS technology configured with a participant's imagery gathering device.

The location data may indicate whether an imagery portion is likely orunlikely to include an attraction. For example, location data that animagery portion that is taken in proximity to the White House mayprovide support for an imagery portion including the White House. On theother hand, a photograph that appears to include the White House, butwas taken in Boston, would be classified as not including the WhiteHouse. In other words, GPS data may at the very least help determinewhether it is more or less likely that a particular imagery portionincludes an attraction.

The location module 224 may rely on GPS signals, signal triangulation,RFID/Bluetooth beacons, Access Point IDs, or the like to determinelocations associated with the received imagery. The exact type of dataor technique used to obtain this location data may vary as long as theobjectives of the embodiments described herein may be accomplished.

The scannable indicia module 226 may receive data regarding a scannedbar code, QR code, or some other type of scannable indicia. For example,certain attractions such as items, locations, or the like, may be markedwith these types of scannable indicia. Accordingly, participants may berequired to scan these indicia to confirm they have actually visited theassociated attraction.

The scoring module 228 may assign each imagery portion a score thatrepresents whether (and to what degree) an imagery portion satisfies ascavenger hunt requirement. Accordingly, portions of imagery that have,say, higher scores or scores above a threshold may be more likely to bedetermined to satisfy a scavenger hunt requirement.

The feedback module 230 may provide feedback to a participant regardingtheir provided imagery. For example, the feedback module 230 may offersuggestions to a participant regarding how to take better imagery. Forexample, based on the analysis of a received imagery portion, thefeedback module 228 may instruct a participant to take a picture of anattraction with better lighting, instruct the participant to move closerto an item or object, to zoom in on an object, or the like.

For example, there may be requirements regarding the quality of imagerygathered. These may include aesthetic thresholds or other types ofrequirements regarding what must be in the imagery. Accordingly, thefeedback module 230 may provide instructions regarding how to gatherimagery that satisfies any aesthetical thresholds.

Referring back to FIG. 1, the credit issuance module 118 may then issueone or more credits or some type of reward to participants thatcompleted the scavenger hunt. These credits may be transmitted to anaccount associated with a participant, and may be a monetary value, agift card, a credit, a cryptocurrency amount, or the like.

The system 100 may be configured to issue credits 118 in a variety ofways. For example, the first participant that finishes gathering imageryof the required attractions may be the only participant that receives acredit. As another example, the participant that gathers imagery of themost amount of the required attractions during a specified time windowmay be the only participant that receives a credit. As yet anotherexample, more than one participant may receive a credit, with morecredits going to participants who gathered more imagery of the requiredattractions than others.

FIG. 3 depicts a flowchart of a method 300 for facilitating a scavengerhunt in accordance with one embodiment. The system 100 of FIG. 1, orcomponents thereof, may perform the steps of method 300.

Step 302 involves receiving at an interface a list of a plurality ofattractions. These attractions may include any one or more of a person,a building, a monument, a landmark, an object, an item, an exhibit, orany other sort of attraction to be captured as part of a scavenger hunt.This list may be provided by a user such as the user 106 or otherwise bysomeone interested in facilitating a scavenger hunt.

Step 304 involves communicating the list of the plurality of attractionsto at least one device associated with a participant over a network.Accordingly, the user may send the required attractions to one or moreparticipants. In some embodiments, the user may be a teacher or a fieldtrip monitor, and the participants may be students who are instructed togather imagery of the required attractions.

The list of the attractions may be communicated to the participants in avariety of ways. For example, and as discussed above, the list may becommunicated via text message, email, SMS, through social media, or thelike. The message may include a link that, upon activation by arecipient, allows the recipient to become a participant in the scavengerhunt.

Step 306 involves receiving imagery from the at least one participantover the network. Step 306 may be performed in a variety of ways. Forexample, once a participant joins the scavenger hunt, theirimagery-gathering device (e.g., a mobile device) may be configured totransmit imagery portions over a network to a processor such as theprocessor 108 of FIG. 1 as the imagery portion is taken. Or, theparticipants may review their gathered imagery before they opt totransmit their imagery to the processor 108 for analysis.

Step 308 involves executing at least one computer vision procedure usinga processor executing instructions stored on memory to determine whetherthe received imagery includes at least one of the plurality ofattractions. Step 308 may involve executing a variety of computervision, machine learning procedures (e.g., neural networks), OCRtechniques, or the like to identify and analyze the content of receivedimagery.

Step 310 is optional and involves providing feedback to at least oneparticipant regarding whether the received imagery includes at least oneof the plurality of attractions. This feedback may offer suggestions toa participant regarding how to take better imagery.

Step 312 is optional and involves issuing a credit to the at least oneparticipant upon determining the received imagery includes at least someof the plurality of attractions. As discussed previously, this mayinclude a reward for completing the scavenger hunt, and may include amonetary value, a gift certificate, a cryptocurrency value, or the like.

The methods, systems, and devices discussed above are examples. Variousconfigurations may omit, substitute, or add various procedures orcomponents as appropriate. For instance, in alternative configurations,the methods may be performed in an order different from that described,and that various steps may be added, omitted, or combined. Also,features described with respect to certain configurations may becombined in various other configurations. Different aspects and elementsof the configurations may be combined in a similar manner. Also,technology evolves and, thus, many of the elements are examples and donot limit the scope of the disclosure or claims.

Embodiments of the present disclosure, for example, are described abovewith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the present disclosure. The functions/acts noted in the blocks mayoccur out of the order as shown in any flowchart. For example, twoblocks shown in succession may in fact be executed substantiallyconcurrent or the blocks may sometimes be executed in the reverse order,depending upon the functionality/acts involved. Additionally, oralternatively, not all of the blocks shown in any flowchart need to beperformed and/or executed. For example, if a given flowchart has fiveblocks containing functions/acts, it may be the case that only three ofthe five blocks are performed and/or executed. In this example, any ofthe three of the five blocks may be performed and/or executed.

A statement that a value exceeds (or is more than) a first thresholdvalue is equivalent to a statement that the value meets or exceeds asecond threshold value that is slightly greater than the first thresholdvalue, e.g., the second threshold value being one value higher than thefirst threshold value in the resolution of a relevant system. Astatement that a value is less than (or is within) a first thresholdvalue is equivalent to a statement that the value is less than or equalto a second threshold value that is slightly lower than the firstthreshold value, e.g., the second threshold value being one value lowerthan the first threshold value in the resolution of the relevant system.

Specific details are given in the description to provide a thoroughunderstanding of example configurations (including implementations).However, configurations may be practiced without these specific details.For example, well-known circuits, processes, algorithms, structures, andtechniques have been shown without unnecessary detail in order to avoidobscuring the configurations. This description provides exampleconfigurations only, and does not limit the scope, applicability, orconfigurations of the claims. Rather, the preceding description of theconfigurations will provide those skilled in the art with an enablingdescription for implementing described techniques. Various changes maybe made in the function and arrangement of elements without departingfrom the spirit or scope of the disclosure.

Having described several example configurations, various modifications,alternative constructions, and equivalents may be used without departingfrom the spirit of the disclosure. For example, the above elements maybe components of a larger system, wherein other rules may takeprecedence over or otherwise modify the application of variousimplementations or techniques of the present disclosure. Also, a numberof steps may be undertaken before, during, or after the above elementsare considered.

Having been provided with the description and illustration of thepresent application, one skilled in the art may envision variations,modifications, and alternate embodiments falling within the generalinventive concept discussed in this application that do not depart fromthe scope of the following claims.

What is claimed is:
 1. A method for facilitating a scavenger hunt, themethod comprising: receiving at an interface a list of a plurality ofattractions; communicating the list of the plurality of attractions toat least one device associated with a participant over a network;receiving imagery from the at least one participant over the network;and executing, using a processor executing instructions stored on memoryto determine whether the received imagery includes at least one of theplurality of attractions, at least one of: a computer vision procedureto analyze content of the received imagery, and a location procedure todetect where the imagery was gathered.
 2. The method of claim 1 furthercomprising receiving location data regarding the imagery for analysis bythe location procedure to at least assist in determining whether thereceived imagery includes at least one of the plurality of attractions.3. The method of claim 1 wherein executing the at least one computervision procedure further includes executing a neural network todetermine content of the received imagery.
 4. The method of claim 1wherein the plurality of attractions include at least one of a point ofinterest, a person, a monument, a landmark, a location, and a building.5. The method of claim 1 further comprising: receiving a required timeperiod, and determining whether the imagery was gathered during therequired time period.
 6. The method of claim 1 wherein the receivedimagery includes at least one of a photograph file, a live photographfile, and a video file.
 7. The method of claim 1 further comprisingexecuting an optical character recognition tool to identify text withinthe imagery and recognize meaning of the identified text.
 8. The methodof claim 1 further comprising issuing a credit to the at least oneparticipant upon determining the received imagery includes the pluralityof attractions.
 9. The method of claim 1 further comprising providingfeedback to at least one participant regarding whether the receivedimagery includes at least one of the plurality of attractions.
 10. Asystem for facilitating a scavenger hunt, the system comprising: aninterface for receiving a list of a plurality of attractions; and aprocessor executing instructions stored on memory and configured to:communicate the list of the plurality of attractions to at least onedevice associated with a participant over a network, receive imageryfrom the at least one participant over the network, and execute, todetermine whether the received imagery includes at least one of theplurality of attractions: a computer vision procedure to analyze contentof the received imagery, and a location procedure to detect where theimagery was gathered.
 11. The system of claim 10, wherein the processoris further configured to receive location data regarding the imagery foranalysis by the location procedure to at least assist in determiningwhether the received imagery includes at least one of the plurality ofattractions.
 12. The system of claim 10 wherein the processor is furtherconfigured to execute a neural network to determine content of thereceived imagery.
 13. The system of claim 10 wherein the plurality ofattractions include at least one of a point of interest, a person, amonument, a landmark, a location, and a building.
 14. The system ofclaim 10 wherein the interface is further configured to receive arequired time period, and the processor is further configured todetermine whether the imagery was gathered during the required timeperiod.
 15. The system of claim 10 wherein the received imagery includesat least one of a photograph file, a live photograph file, and a videofile.
 16. The system of claim 10 wherein the processor is furtherconfigured to execute an optical character recognition tool to identifytext within the imagery and recognize meaning of the identified text.17. The system of claim 10 wherein the system is further configured toissue a credit to the at least one participant upon determining thereceived imagery includes the plurality of attractions.
 18. The systemof claim 10 wherein the processor is further configured to providefeedback to at least one participant regarding whether the receivedimagery includes at least one of the plurality of attractions.